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@ARTICLE{Bolt:874723,
      author       = {Bolt, Taylor and Nomi, Jason S. and Arens, Rachel and Vij,
                      Shruti G. and Riedel, Michael and Salo, Taylor and Laird,
                      Angela R. and Eickhoff, Simon B. and Uddin, Lucina Q.},
      title        = {{O}ntological {D}imensions of {C}ognitive-{N}eural
                      {M}appings},
      journal      = {Neuroinformatics},
      volume       = {18},
      issn         = {1559-0089},
      address      = {New York, NY},
      publisher    = {Springer},
      reportid     = {FZJ-2020-01635},
      pages        = {451–463},
      year         = {2020},
      abstract     = {The growing literature reporting results of
                      cognitive-neural mappings has increased calls for an
                      adequate organizing ontology, or taxonomy, of these
                      mappings. This enterprise is non-trivial, as relevant
                      dimensions that might contribute to such an ontology are not
                      yet agreed upon. We propose that any candidate dimensions
                      should be evaluated on their ability to explain observed
                      differences in functional neuroimaging activation patterns.
                      In this study, we use a large sample of task-based
                      functional magnetic resonance imaging (task-fMRI) results
                      and a data-driven strategy to identify these dimensions.
                      First, using a data-driven dimension reduction approach and
                      multivariate distance matrix regression (MDMR), we quantify
                      the variance among activation maps that is explained by
                      existing ontological dimensions. We find that 'task
                      paradigm' categories explain more variance among
                      task-activation maps than other dimensions, including latent
                      cognitive categories. Surprisingly, 'study ID', or the study
                      from which each activation map was reported, explained close
                      to $50\%$ of the variance in activation patterns. Using a
                      clustering approach that allows for overlapping clusters, we
                      derived data-driven latent activation states, associated
                      with re-occurring configurations of the canonical
                      frontoparietal, salience, sensory-motor, and default mode
                      network activation patterns. Importantly, with only four
                      data-driven latent dimensions, one can explain greater
                      variance among activation maps than all conventional
                      ontological dimensions combined. These latent dimensions may
                      inform a data-driven cognitive ontology, and suggest that
                      current descriptions of cognitive processes and the tasks
                      used to elicit them do not accurately reflect activation
                      patterns commonly observed in the human brain.},
      cin          = {INM-7},
      ddc          = {540},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {32067196},
      UT           = {WOS:000516229200001},
      doi          = {10.1007/s12021-020-09454-y},
      url          = {https://juser.fz-juelich.de/record/874723},
}